[Rasch] Rasch-inspired open-source software now available for highly multidimensional data
Iasonas Lamprianou
liasonas at cytanet.com.cy
Tue Apr 3 04:47:52 EST 2012
That is a very interesting package. Thank you for the detailed response
----- Original Message Follows -----
From: Mark Moulton <markhmoulton at gmail.com>
To: liasonas at cytanet.com.cy
Cc: rasch at acer.edu.au
Subject: Re: [Rasch] Rasch-inspired open-source software now available for highly multidimensional data
Date: Mon, 2 Apr 2012 11:16:20 -0700
> Hi Jason,
>
> (a) *Short answer:* Damon does not handle regression in the way you're
> thinking, in the style of Rasch latent regression where (say) person
> properties are used to predict Rasch ability measures. It does not return
> regression effect coefficients for predictor variables. For that, you'll
> want another tool.
>
> *Long answer:* Damon *does* include all person properties in the analysis
> as if they were simply additional items -- gender, age, etc, are items
> along with MathItem1, MathItem2, EngItem1,EngItem2, etc. In other words,
> predictor variables and dependent variables are not treated differently --
> each is a predictor of the others. (Multicollinearity is not a problem
> because Damon internally projects all items and persons into a subspace of
> constrained dimensionality, and the orthornormal/linearly independent
> coordinates of that space become the true predictor variables.)
>
> What this means is that Damon is very good at *predicting* values for a
> given dependent variable (assuming fit to the model), if that is your goal.
> It includes a method for removing any biases that might be caused by a
> dependent variable helping predict itself. So the primary output is a
> matrix of predictions of each person on each item, given the information
> provided by the rest of the dataset. Secondary outputs are the coordinates
> of each item and person. The dot product of these coordinates supplies the
> prediction. Damon takes the strong position that regression coefficients
> of predictor variables are *insufficient* to predict outcomes. One must
> also have computed the latent coordinates of the individual persons for
> whom predictions are being made, in order to preserve the sample-free
> property of the predictions. That is why Damon does not report regression
> style effect sizes.
>
> (b) Damon currently only supports 2-facet (tabular) designs. I'm eager to
> build a many-facet version, but that will be a total rewrite and may kill
> me.
>
> Mark
> www.pythiasconsulting.com
>
>
>
>
> 2012/4/2 Iasonas Lamprianou <liasonas at cytanet.com.cy>
>
> > Hi,
> > (a) can the Damon software be used to solve regression-like problems? For
> > example, can I run the ussual Rasch model, also using independent variables
> > for latent regressions?
> > (b) Does it support many-facets designs?
> >
> > Thanks
> >
> > Jason
> >
> > ----- Original Message Follows -----
> > From: Mark Moulton <markhmoulton at gmail.com>
> > To: <rasch at acer.edu.au>
> > Subject: [Rasch] Rasch-inspired open-source software now available for
> > highly multidimensional data
> > Date: Fri, 23 Mar 2012 20:06:14 -0700
> > > *Dear Rasch Colleagues,*
> > >
> > > I want to let you know about *Damon*, a Python-based software package
> > that
> > > I'm releasing free to the Rasch psychometric community and general
> > public.
> > > The result of some 20 years hard labor (initiated by Ben Wright to his
> > > surprise), it has been in private commercial use for the last five years.
> > > It was designed specifically to apply Rasch's objectivity criterion to
> > *highly
> > > multidimensional* datasets, such as the infamous Netflix movie ratings
> > > dataset.
> > >
> > > It is documented and available for download through
> > > www.pythiasconsulting.com . If there is any interest, I will hold a
> > *Damon
> > > workshop in Vancouver* at my hotel on the morning of *Friday, April
> > 13,*after
> > > *IOMW*.
> > >
> > > Contact me if you have any questions.
> > >
> > > Thanks!
> > >
> > > Mark H. Moulton, Ph.D.
> > > Pythias Consulting
> > >
> > > markhmoulton at gmail.com
> > > 408-307-2794
> > >
> > >
> > > *Features/Bugs (depending on point of view):*
> > >
> > > - *Multidimensionality.* Easily handles data with 1, 5, 10, 50, 100+
> > > dimensions, including items that are negatively correlated.
> > > - *Data types.* Handles interval, ordinal, ratio, dichotomous,
> > > polytomous, and nominal data, including within the same dataset.
> > > - *Missing Data. * Handles pretty much any proportion of missing data,
> > > random or non-random.
> > > - *Labels.* Datasets support row labels and column labels of any
> > depth.
> > > Queries are label-based.
> > > - *Objectivity.* Damon offers clear criteria for determining and
> > > optimizing the objectivity of all reported statistics, including "best
> > > dimensionality". These include Rasch's parameter invariance
> > requirement.
> > > - *Measures.* Person measures consist of cell estimates, in either
> > the
> > > original or a linear (logit) metric, averaged across one or more items
> > > within a dataset. They answer the question: *How able is Person A on
> > > the construct embodied by a defined subset of items?*
> > > - *Predictions.* Predictions are of the form: *How would Person A
> > have
> > > performed on Item 1 if he had taken the item?*
> > > - *Equating.* Parameters from one dataset are transferrable as
> > anchors
> > > to a comparable dataset.
> > > - *Analysis of Fit.* Damon reports fit statistics, standard errors,
> > > etc., for determining the degree to which the observed data fits into
> > the
> > > objective space.
> > > - *Rasch.* Damon includes a Rasch module based on the Winsteps JMLE
> > > implementation.
> > >
> > > *Algorithm*
> > >
> > > - *ALS/Rasch.* The algorithm is classified as a multidimensional
> > > alternating least squares matrix decomposition subjected to a strict
> > > Rasch-based objectivity optimization criterion.
> > >
> > > *Usability*
> > >
> > > - *Python.* Damon is built on top of the Python scripting language
> > and
> > > the Numpy numerical package (both free). It is accessed through a
> > > command-line shell or run from scripts. If you have experience with
> > the
> > > statistical language *R*, or *MatLab*, you will feel at home.
> > Although
> > > Damon is written in Python, it requires very little expertise in
> > Python
> > > programming. The website includes a tutorial. Damon's inline help
> > > resources are extensive.
> > >
> > > *A Sample Damon script*
> > >
> > > >>> import damon1.core as dmn
> > > >>> Data =
> > >
> > dmn.DamonObj('California_May2012.csv','TextFile',nHeaders4Rows=5,nHeaders4Cols=1,ValidChars=['All',[0,1]])
> > > >>> Data.standardize()
> > > >>> Data.coord([range(1,11)],RunSpecs=[0.0001,20])
> > > >>> Data.baseEst()
> > > >>> Data.finEst()
> > > >>> Data.summStat()
> > > >>> Data.export(['summStat_out','baseEst_out','finEst_out'],'TextFile')
> > >
> > > This short program imports and formats a text file, standardizes the
> > data,
> > > looks for the optimal (most objective) dimensionality in a range of 10
> > > dimensions, computes coordinates (multidimensional abilities and
> > > difficulties) for each person and item, computes an array of cell
> > estimates
> > > (for both missing and non-missing cells), computes another array of cell
> > > (0,1) predictions, computes person measures, and exports three of the
> > > outputs as text files.
> > >
> > > Various other statistics, functions, and methods are available in Damon,
> > > plus the massive libraries in Numpy and related Python packages.
> > >
> > >
> > > _______________________________________________
> > > Rasch mailing list
> > > Rasch at acer.edu.au
> > > Unsubscribe:
> > https://mailinglist.acer.edu.au/mailman/options/rasch/liasonas%40cytanet.com.cy
> >
>
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